Hair cortisol as a viable tool for the assessment of an association between environmental noise exposure and chronic stress
Bibliographic record
Abstract
Entrenched in the well-established link between stress and health, noise exposure as a potential contributor to stress-related health effects receives tremendous attention. Indeed, exposure to noise can act as a stressor as evidenced through increased heart rate, blood pressure, adrenaline, epinephrine, and cortisol. Cortisol is secreted from the adrenal glands in response to stressor-induced activation of the hypothalamic-pituitary-adrenal axis. For assessment of environmental noise and stress, repeated sampling in blood, saliva, or urine is necessary to evaluate the association between environmental noise exposure and protracted changes in cortisol. Controlling for the many variables that influence the secretion of cortisol at discrete sampling intervals is challenging. Studies suggest that systemically produced cortisol integrates and remains in hair as it grows, providing a measure that integrates a cortisol response over a longer period, circumventing several limitations associated with multiple sampling. Robust evidence supports the integration of cortisol into hair, yet recent studies call into question the notion that cortisol is retained with growth. The current paper discusses the strengths and limitations of hair cortisol analysis with an emphasis on its utility as a measure of chronic stress in environmental noise studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".